Scaling Up IoT Stream Processing

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Scaling Up IoT Stream ProcessingTaegeon UmGyewon LeeSanha LeeSeoul National UniversitySeoul National UniversitySeoul National UniversityKyungtae KimByung-Gon ChunSeoul National UniversitySeoul National UniversityABSTRACTUsers create large numbers of IoT stream queries with datastreams generated from various IoT devices. Current streamprocessing systems such as Storm and Flink are unable tosupport such large numbers of IoT stream queries efficiently,as their execution models cause a flurry of cache misses whileprocessing the events of the queries. To solve this problem, wepresent a new group-aware execution model, which processesthe events of IoT stream queries in a way that exploits thelocality of data and code references, to reduce cache missesand improve system performance. The group-aware execution model leverages the fact that users create the groupsof queries according to their interests or location contextsand that queries in the same group can share the same dataand codes. We realize the group-aware execution model onMIST—a new stream processing system tailored for processing many IoT stream queries efficiently—to scale up thenumber of IoT queries that can be processed in a machine.Our preliminary evaluation shows that our group-aware execution model increases the number of queries that can beprocessed within a single machine up to 3.18 compared tothe Flink-based execution model.ACM Reference format:Taegeon Um, Gyewon Lee, Sanha Lee, Kyungtae Kim, and ByungGon Chun. 2017. Scaling Up IoT Stream Processing. In Proceedingsof APSys ’17, Mumbai, India, September 2, 2017, 7 ODUCTIONInternet of Things (IoT) devices generate various real-timedata streams in diverse places, such as real-time temperaturePermission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies are notmade or distributed for profit or commercial advantage and that copies bearthis notice and the full citation on the first page. Copyrights for componentsof this work owned by others than the author(s) must be honored. Abstractingwith credit is permitted. To copy otherwise, or republish, to post on servers orto redistribute to lists, requires prior specific permission and/or a fee. Requestpermissions from [email protected] ’17, September 2, 2017, Mumbai, India 2017 Copyright held by the owner/author(s). Publication rights licensed toAssociation for Computing Machinery.ACM ISBN 978-1-4503-5197-3/17/09. . . 15.00 homes [11, 15], fertility in farms [24], ball movement instadiums [10], the number of cars in parking lots [13], andmuch more. From these data streams, users can create IoTstream queries—continuously processing data streams generated from their IoT devices—to obtain useful informationor to control their IoT devices with low latency. These IoTstream queries handle a small amount of data streams related to users’ interests. For instance, users can create queriesthat notify them of break-in at their home by inspecting thedata streams generated from their home doors, or that adjustthe fan speed of the air conditioner by analyzing the roomhumidity and temperature data streams.As users have various query requests on diverse IoT devicesdeployed, they create many stream queries that are tailored totheir concerns. If the number of users is 10 million, with eachuser creating 100 queries, then the number of queries becomes1 billion. Thus, the number of IoT stream queries is huge. Thisworkload—large numbers of small queries—is very differentfrom the workloads that current stream processing systemstarget: small number of stream queries that process a largeamount of data.Current stream processing systems (SPSs) [8, 23, 27] suchas Storm, Flink, and Spark Streaming do not efficiently support large numbers of IoT stream queries efficiently, becausetheir execution models cause a flurry of cache misses for thisworkload. When processing stream queries, they create separate processes or threads per query. This design works wellon the distributed execution of big queries. However, whenrunning large numbers of IoT stream queries, this executionmodel causes frequent context switching among threads (orprocesses). Since each thread holds the data of each query,such as input data streams, internal query states and codes,the frequent context switching increases the working set sizein a CPU, resulting in frequent cache misses, which in turnincreases CPU use and hinders SPSs from processing manyIoT stream queries.To solve this problem, we take advantage of the fact thatusers can naturally create groups of IoT queries accordingto the users’ intentions. For example, a user who intends tocontrol her house can create a home group and add her homecontrol IoT queries to the group. Queries inside the samegroup can process common data streams and have commonapplication codes (logic). For example, in the home group,

APSys ’17, September 2, 2017, Mumbai, Indiaqueries that adjust the air conditioner or the heater according to the current home temperature process the same hometemperature data stream. In addition, queries adjusting thetemperature of different rooms inside the home can use thesame code to modify the room temperatures.In this paper, we design a new group-aware executionmodel that processes the events of queries in a way that exploits the locality of data and code references. The groupaware execution model enables a single thread to consecutively process all the events of queries within the same group.This execution model reduces CPU cache misses and improves system performance, since the data and code residingin the CPU cache will be reused. We realize the group-awareexecution model on MIST—a new stream processing systemaimed at supporting large numbers of IoT stream queries in acluster of machines. The group-aware execution model scalesup MIST to increase IoT queries that can be processed in asingle machine; thus it helps MIST minimize the necessarynumber of machines to process billions of IoT queries. Ourpreliminary evaluation shows that the group-aware executionmodel improves the maximum number of stream queries thatcan be processed in a single machine up to 3.18 , comparedto the Flink-based execution model, while maintaining themedian latency below 10 ms.2BACKGROUND AND MOTIVATIONIn this section, we describe the execution models of currentstream processing systems (SPSs) by investigating two popular streaming frameworks: Storm [23] and Flink [8]. We showthe limitations of their execution models in dealing with largenumbers of IoT stream queries.2.1Directed Acyclic GraphModern SPSs [8, 23, 27] are designed for users to easily runbig stream queries on distributed environments. They represent a stream query as a data-flow DAG (directed acyclicgraph). In a DAG, a vertex (v) is either a source (s), an operator (o), or a sink (k). An edge (v x vy ) represents thestream of data flowing from the upstream vertex (v x ) to thedownstream vertex (vy ). We explain the details of the datastream and each type of vertex as follows: Data Stream: A data stream consists of continuousevents, and each event is a pair of a value and a timestamp. The value holds real-world information, such asthe current temperature or location, and the timestampspecifies the time of event generation. Source: A source is the root vertex of a DAG, and itfetches or receives a data stream from external systems,such as Kafka [14] or MQTT Broker [7]. It sends inputevents to downstream operators. This operation is I/Obound because it receives data from the network.Taegeon Um et al. Operator: An operator is the intermediate vertex thathas incoming and outgoing edges. Operator receivesevents, processes their values according to the definedoperation (filter, map, windowing, aggregation, or userdefined function), and it emits the processed events asinput events to downstream vertices. In general, operations, such as filters, maps, windowing, or aggregates,are CPU-bound computations. Sink: A sink is the leaf vertex of a DAG, and emitsinput events to external systems. It is I/O bound becauseit sends data through the network.2.2Execution Model and LimitationsWe explain the execution models of two popular streamingsystems—Storm and Flink—to show how they execute DAGs.After that, we investigate their limitations of processing largenumbers of IoT stream queries.2.2.1 Execution Model. Storm and Flink are good atprocessing a rather small number of big queries in a distributed manner, by creating separate processes (in Storm)and threads (in Flink) per DAG.Storm [23] represents the DAG of a query as a topologyconsisting of two component types: spouts and bolts. A spoutis mapped to a source, and a bolt is mapped to an operatoror a sink. To run a topology, Storm creates one or more JVMprocesses, called Workers, and distributes spouts and boltsacross these Workers. Each Worker maintains several threads,called Executors, and each Executor has an incoming andoutgoing event queue for a component (spout or bolt). Whenevents sent from the upstream vertex are enqueued to theincoming event queue, the Executor thread processes theevents and sends them to the outgoing event queue. Spoutsreceive events from the external system and send them tothe outgoing event queue. If there is no event to process inthe incoming queue, the Executor thread sleeps until anotherevent arrives.Flink chains operators in a DAG and creates a DAG ofTasks, where each Task runs on the long-running runtimeprocesses, called TaskManagers. A TaskManager can runmultiple Tasks from different queries. In the TaskManager,each Task is executed by one thread, and the thread processesthe events of the vertex. As with Storm, each Task thread hasan incoming event queue, which sleeps when there is no eventto process in the queue.2.2.2 Limitations. Creating multiple processes and threads per query causes an excessive number of context switching among them, which increases the working set size andleads to frequent cache misses. Thus, the CPU becomes abottleneck.

Thp (103events/sec)Scaling Up IoT Stream Processing120Ideal ThpAPSys ’17, September 2, 2017, Mumbai, IndiaMeasured Thp80400020406080Number of queries (100103120140)Figure 1: Performance evaluation of the Flink-based executionmodelAs the number of queries increases in SPSs, the number ofthreads or processes also increases. If the number of threads islarge, the number of context switching also increases. In addition, since each IoT stream query processes a small amount ofdata streams, the threads sleep and wake up repeatedly, whichalso increases the frequency of context switching. Commonly,IoT devices generate events at a certain interval (e.g., 1 second). After a thread processes an event in a CPU, it waitsuntil another event is generated. In the meanwhile, anotherthread can process the event of a different query in the sameCPU. As each thread handles each query, which has its owndata, such as data streams, codes and internal operator states,multiple threads that process the events of different queries inthe same CPU will increase the working set size. Eventually,a large working set will not fit into a CPU cache and lead to aflurry of cache misses.To understand this problem, we evaluate how Flink performance degrades as the number of queries (threads) increasesin a single machine (a single TaskManager). Storm evidentlysupports a smaller number of queries than Flink does becauseStorm executes queries in a heavier way than Flink (processesper query vs threads per query); thus, we choose Flink as abaseline.We implement two categories of streaming queries for theevaluation: Abnormal heart rate detection (AHR) and Point ofInterest recommendation (PoI). AHR query consistently detects abnormal heart rates based on the user’s activity, and PoIquery recommends nearby points of interest according to theuser’s current GPS location. To emulate IoT data streams, weuse two datasets: GeoLife [30] and PAMAP2 [21]. PAMAP2is a dataset containing user’s activity, motion, and heart ratedata; and GeoLife is a collection of GPS trajectory logs frompeople in Beijing. We generate an event per second on average, following a Poisson process model. Each AHR and PoIquery consumes a data stream generated from PAMAP2 andGeoLife, respectively. EMQ [7] is used as a message brokerfor the reliable large-scale delivery of these events.We gradually increase the number of AHR and PoI queriesand measure throughput (the number of processed events persecond). The measured throughput should be proportionalto the number of queries, since each query processes oneevent per second, on average. In all cases of the experiments,we use a 28-core NUMA machine (2 Intel Xeon E5-26802.4GHz, 35M Cache, 8 16GB RDIMM) to run MIST. FlinkTaskManager runs on the machine, and emulated sources,message brokers, and result loggers run on separate machines.Flink was able to handle approximately 4K queries, withthe main bottleneck being the cost to maintain thousands ofnetwork connections. Flink deals with each query separately,so a large number of network connections are created whenusers submit many queries. Maintaining each network connection is expensive; thus it degrades system performance.To investigate the problems concerning the execution model(thread-per-query) clearly, we implement a Flink-based execution model that addresses the network bottleneck by sharingthe network connections among queries while creating a newthread per operator, based on the Flink execution model. Figure 1 shows that the Flink-based execution model handlesup to approximately 110K stream queries, before the measured throughput significantly degrades. This is because theFlink-based execution model has a CPU bottleneck, causedby the higher number of cache misses. We present the detailed numbers, such as the number of cache misses and CPUuse compared to the MIST group-aware execution model, in§ 3.5. In the following sections, we discuss how the MISTgroup-aware execution model reduces the number of cachemisses and improves system performance.3GROUP-AWARE EXECUTION MODELIn contrast to Flink, which creates new threads per queryand makes the OS scheduler responsible for scheduling theevent processing, we design a group-aware execution modelthat exploits the locality of data and code references whileprocessing the events of queries in the same group. Whenusers create groups of queries, queries in the same group canprocess the same data stream and have the same code. Ourmain idea is to group events of different queries belongingto a single group and process them consecutively in a singlethread. This approach better exploits the locality of data andcode references within the groups and improve the systemperformance, as we can reuse the same data and code residingin the CPU cache.3.1MIST OverviewBefore illustrating the group-aware execution model, we givea brief overview of MIST, which is a new stream processing system designed to process large numbers of IoT streamqueries in a cluster of machines. We realize the group-awareexecution model on MIST and enable MIST to increase thenumber of queries that can be processed in a single machine.

APSys ’17, September 2, 2017, Mumbai, IndiaQuery Q1 (Group G1)s1s2q1q2s1s2s3s4G1G2o1o2s5 Query Q2 (Group aegeon Um et al.G4.T2k1k3k2k4G1G2 A fixed number of threadsSource StageOperator StageSink StageFigure 2: The architecture overview of the group-aware execution model in a stream engine. s x , o x , k x , q x , and Tx representsa source, operator, sink, event queue, and an operator thread.Overall, MIST consists of three components: the front end,the driver, and the stream engines.Front End. The MIST front end enables users to createand manage queries. It also provides an interface for usersto group their queries by labeling them according to theirpurpose. Each query is converted to a DAG and submitted tothe MIST driver.Driver. When the DAG of a query is submitted from theMIST front end, the MIST driver receives the DAG and assigns it to a stream engine out of the engines available. Eachstream engine is a process that runs on a single node and handles multiple stream queries. To process all of the queries inthe same group by leveraging the group information, the driver assigns the queries in the same group to the same streamengine.Stream Engines. A stream engine is a process that runs ona single node, and it is in charge of processing multiple IoTstream queries. It processes the events of queries accordingto the defined DAGs. We apply the group-aware executionengine in the MIST stream engine to increase the number ofIoT stream queries processed in a machine. Next we presentthe details of the group-aware execution model.3.2Stage and Query SeparationFigure 2 shows the overview of the group-aware executionmodel in a MIST stream engine. A MIST stream engine consists of three separate stages: the source stage, operator stage,and sink stage. The threads of each stage are independent;hence, by separating the threads for I/O operations (sourcesand sinks) and CPU operations (operators), we can use the I/Oand CPU operations effectively [26]. Each stage has a fixednumber of threads (can be configured by system administrators) to reduce frequent context switching and cache missesthat can occur in a large number of threads and to realize thata single thread processes the events of multiple queries in thesame group.To separate the operation of sources, operators, and sinks,MIST adds internal event queues to the DAG between a sourceand an operator and between an operator and a sink. In Figure 2, query Q1 has internal operator event queues betweens1 and o1 and between s2 and o2. MIST also adds a sink eventqueue between o3 and k1. The source thread receives andsends events to the operator event queues, the operator threadprocesses the events by applying the functions of downstreamoperators in depth-first search order, and sends the processedevents to the sink event queues; the sink thread then emits theevents to external systems.In following sections, we focus on the operator stage because processing the events of operators is the main bottleneck of the system. We explain how MIST assigns groupsand queries to an operator thread and processes events in theoperator stage.3.3Group and Query AssignmentTo process the events of a group, MIST first assigns a newgroup to a thread, and whenever a new query for the group iscreated, MIST adds the operator event queue of the query tothe group. As an example of the assignment of operator eventqueues, in Figure 2, MIST assigns group G1 to thread T 1 andallocates the operator event queues of Q1 and Q2 (q1, q2, andq3) to G1, because queries Q1 and Q2 are included in groupG1. Then, thread T 1 will process the incoming events of allqueries within group G1.Our group assignment policy is to balance the number ofoperator event queues in the operator stage. For instance, inFigure 2, T 1 has two assigned groups (G1 and G2) and sixoperator event queues; whereas T 2 has two assigned groups(G3 and G4) and five operator event queues. When a newgroup and queries are created, MIST will assign the newgroup to T 2 in order to balance the number of operator eventqueues. This assignment policy works well in a situationwhere the size of groups (the number of queries) does notfrequently change, and the incoming event rate and requiredcomputations for each query are similar. In § 4, we will furtherdiscuss another assignment policy for other situations.3.4Group-Aware Event ProcessingThe remaining part is about how each operator thread processes events. Each operator thread has several assignedgroups because the number of threads is less than the number of groups in general. At a high level, to schedule theevents of assigned groups in a way that exploits the locality of

34567891011data and code references, the operator thread picks an activegroup, which has at least one event to be processed, processesthe events of the active group until there is no event to beprocessed, and repeats this process.Algorithm 1 shows this event processing mechanism. Athread picks an active group from the active group queue (line:3). For the active group queue, we use a blocking queue inorder not to waste CPU cycles when there is no event to beprocessed. The thread sleeps if there is no active group. Towake up the thread, we create a group dispatcher thread thatadds active groups to the active group queue by iterating thelist of assigned groups to a thread. For example, in Figure 2,T 2 sleeps because it has no active group. When an eventis generated and enqueued to q9, G3 becomes active, andthe group dispatcher will add G3 to the group schedulingqueue of T 2. Then, T 2 will wake up and process the event.After selecting a group, the thread selects an active operatorevent queue that has at least one event (line: 8). After that, itprocesses all of the events from the operator event queue untilit becomes empty. The operator event queue will be added tothe operator scheduling queue again when an event for theoperator event queue is created.This event scheduling mechanism allows a thread to process the events of an operator successively, as well as theevents of operators in the same group consecutively. Asqueries in the same group can have the same data streamand code, MIST can reuse recently referenced data and codesand reduce cache misses.With a low probability, operator threads could be occupiedby an active group where events are continuously generated,which would mean events in other active groups could notbe processed by the threads for a long time. MIST preventsthis situation by preempting the active group when the eventprocessing time of the group is larger than the preemptiontimeout (pTimeout, line 7).400K300K200KMax # of queriesMedian latency100K0KFlink-basedMIST group-aware100806040200Figure 3: Performance of the Flink-based execution model andMIST group-aware execution model(a) MISTgroup-awareCPU Util.2Function PROCESSING(activeGroupQueue, pTimeout)while not finished do// Sleep if the queue is emptygroup activeGroupQueue.take();opSchedQueue group.getOpSchedQueue();startTime currentTime();while group.hasEvent() andelpasedTime(startTime) pTimeout doopEventQueue opSchedQueue.poll();while opEventQueue.hasEvent() doevent opEventQueue.poll();processEventInDFS(event);Max. # of queries1# of LLC misses (109 )Algorithm 1: Event Processing MechanismAPSys ’17, September 2, 2017, Mumbai, IndiaMedian latency (ms)Scaling Up IoT Stream Processing(b)60.0%40.0%20.0%0.0%Flink-based MISTgroup-awareFigure 4: (a) shows the number of last-level cache (LLC) misseswhile processing 40K queries during 2 minutes and (b) showsthe CPU use while processing 40k queries during 2 minutes inthe Flink-based execution model and MIST group-aware execution model3.5Preliminary EvaluationWe evaluate the performance of the MIST group-aware execution model in the same environment used for Flink evaluation § 2.2.2. We set the number of threads in the source,operator, and sink stages to 100, 56 (2 the number of cores),and 100, respectively. To emulate query groups, we groupn100 AHR and PoI queries, i.e., the number of groups was 100,where n is the number of queries.Figure 3 shows that the MIST group-aware execution modelimproves the number of IoT stream queries processed in amachine up to 350K with 6ms median latency; 3.18 largernumber of queries compared to the Flink-based executionmodel. These results demonstrate that our group-aware execution model reduces cache misses and improves systemperformance. Figure 4(a) shows that the Flink-based execution model has a 3.19 higher number of last-level cache(LLC) misses compared to MIST in processing 40K queriesin 2 minutes. The higher number of cache misses leads to inefficient CPU use in the Flink-based execution model, whichis illustrated in Figure 4(b). At the same number of queries,the CPU use of the Flink-based execution model is 30.2%,whereas that of the MIST group-aware execution model is13.8%.

APSys ’17, September 2, 2017, Mumbai, India4DISCUSSIONWe discuss interesting research directions below to more scaleup IoT stream processing in MIST.Reducing Duplicate Computations. In plenty of IoT stream queries, some queries can generate duplicate computations and this could be a cause of high CPU use. MISTcan reduce these duplicate operations by merging queriesthat have the same computations. Sharing operations amongmultiple queries has been explored in the streaming databasefield [5, 9, 17, 20, 25, 28, 29], focusing on optimizing computations that have different parameters, such as differentwindow sizes or different filter predicates. MIST can also usethese techniques to merge queries. However, with billions ofstream queries, finding the queries to merge could be timeconsuming. To solve this challenge, we can also leverage thegroup information to identify mergeable queries. By limitingthe search space within the same group, we can identify themquickly with negligible overhead.Load Balancing in Group Assignment. Current group assignment policy supposes that an operator thread load is proportional to the number of queries. However, load imbalancecan occur if the load is not proportional to the number ofqueries: the incoming event rate and amount of computationof each query are different. The load imbalance can increasethe latency and degrade the system performance; thus, groupassignment should consider the load of threads, which depends on the event rate, amount of computation, etc. As anexample, queuing theory can be used to measure the load ofthreads [6]. Then, assigning new groups to the thread that hasthe lowest load balances the load among threads.Group Reassignment. Even when we balance the load ofthreads while assigning groups, load imbalance could stilloccur over time as we pin a group to a certain thread. Forinstance, the number and the event incoming rate of queriesin a group can change over time after the group is assigned toa thread. A high-level policy to mitigate the load imbalanceamong threads is to reassign the groups from overloadedthreads to underloaded threads. To develop the reassignmentpolicy, we should consider several details, including how todetermine overloaded and underloaded threads, and whichgroups should be reassigned. These issues must be addressedwhile minimizing the number of groups to be reassigned,because processing events of groups in different threads islikely to increase the number of cache misses.Reducing Memory Use. Some operators have internal states, such as windows or aggregates. When the size of persisting states for each query becomes large, memory can becomea bottleneck. Unloading (removing) the states of inactivequeries from memory to disk is a feasible solution to solve thememory bottleneck, because some IoT queries could becomeinactive for a long time. For instance, GPS queries that trackTaegeon Um et al.bicycle location are only active when users are riding bicycles. However, with large numbers of queries, deciding whichqueries are inactive is challenging. We can again addressthis challenge using the group information. Since queries inthe same group can share data streams, queries that processthe same data streams are likely to become inactive at thesame time, when the data streams become inactive. Hence,MIST could track whether a group is active or inactive and(un)load the whole queries in the same group. This approachcan reduce the overhead of tracking each individual querystatus.5RELATED WORKStream Processing Systems. The limitations of currentstream processing systems [8, 23, 27] are discussed in § 2.2.2.Streaming Databases. Streaming databases [1–5] are designed to process data streams. They focus on streamingqueries that process data streams in which the data format isrelational data (e.g., uniform schema). Their target is differentfrom MIST, which does not limit the data format generatedfrom various IoT devices. In addition, they do not leveragethe group information and exploit the locality of data andcode references to scale up the number of queries processedin a machine.Sensor Networks. Sensor networks aggregate data streamsfrom geographically distributed sensors. Most sensor networkcommunities focus on data aggregation in the network toreduce the communication overhead [16, 18]. In contrast tothem, our group-aware execution model focuses on centralized processing of IoT stream queries in the back-end server.IoT Platforms. There are commercial solutions providing an integrated stack for developing IoT platforms, suchas Azure IoT Suite [19], AWS Internet of Things [22], orIFTTT [12]. As these platforms need to serve more and morestream queries, the MIST group-aware execution model canbe used to scale up stream processing in these systems.6CONCLUSIONWe propose MIST, a new stream processing system that isdesigned to efficiently handle large numbers of IoT streamqueries. To scale up the number of queries that can be processed in a machine in MIST, we design a new group-awareexecution model that exploits the locality of data and code references in the same group. Our preliminary evaluation showsthat the MIST group-aware execution model improves thenumber of IoT stream queries up to 3.18 compared to theFlink-based execution model. We believe that MIST offersnew, interesting opportunities for research to improve IoTstream processing.

Scaling Up IoT Stream ProcessingACKNOWLEDGEMENTSWe thank anonymous reviewers for their comments. We alsothank Brian Cho, Zhenping Qian, Jooyeon Kim, WonwookSong, Hyunmin Ha, and Jangho Seo for their feedback. Thisresearch was supported by Samsung Research Funding Centerof Samsung Electronics under Project Number

Byung-Gon Chun Seoul National University ABSTRACT Users create large numbers of IoT stream queries with data streams generated from various IoT devices. Current stream processing systems such as Storm and Flink are unable to support such large numbers of IoT stream queries efficiently, as their execution models cause a flurry of cache misses while

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Stylus is a low-level stream processing framework written in C . The basic component of Stylus is a stream pro-cessor. The input to the processor is a Scribe stream and the output can be another Scribe stream or a data store for serving the data. A Stylus processor can be stateless or stateful. Processors can be combined into a complex pro .

scaling factor, which allows faster circuit operations. Figure 2:2 also depicts the increase in the density of transistors in SRAM and Logic circuits. Density is inversely proportional to the total chip area ( 1/A ). Therefore, density will increase by κ2 as a result of scaling, where κ 2 is the scaling constant (see section 2.1.4).

Alfredo López Austin TEMARIO SEMESTRAL DEL CURSO V. LOS PRINCIPALES SISTEMAS DEL COMPLEJO, LAS FORMAS DE EXPRESIÓN Y LAS TÉCNICAS 11. La religión 11.1. El manejo de lo k’uyel. 11.1.1. La distinción entre religión, magia y manejo de lo k’uyel impersonal. Los ritos específicos. 11.2. Características generales de la religión mesoamericana. 11.3. La amplitud social del culto. 11.3.1 .