Edge/Fog Computing is an extension to the Cloud Computing model, primarily proposed to pull some of the load on cloud data center towards the edge of the network, i.e., closer to the clients. Despite being a promising model, the foundations to adopt and fully exploit the edge model are yet to be clear, and thus new ideas are continuously advocated. In his paper on \Life beyond Distributed Transactions: An Apostate's Opinion", Pat Helland proposed his vision to build\almost innite" scale future applications, demonstrating why Distributed Transactions are not very practical under scale. His approach models the applications data state as independent \entities" with separate serialization scopes, thus allowing ecient local transactions within an entity, but precluding transactions involving dierent entities. Accessing remote data (which is assumed rare) can be done through separate channels in a more message-oriented manner. In this paper, we recall Helland's vision in the aforementioned paper, explaining how his model ts the Edge Computing Model either regarding scalability, applications, or assumptions, and discussing the potential challenges leveraged .Project “TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020”is financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).info:eu-repo/semantics/publishedVersio

Almeida, Paulo Sérgio

Baquero, Carlos

Kassam, Ziad

Shoker, Ali

Universidade do Minho: RepositoriUM

Life Beyond Distributed Transactions on the Edge[Draft of ACM paper]Ali ShokerHASLab, INESC TEC & Minho UniversityBraga, Portugalali.shoker@inesctec.ptZiad KassamHASLab, INESC TEC & Minho UniversityBraga, Portugalzakassam@inesctec.ptPaulo Sérgio AlmeidaHASLab, INESC TEC & Minho UniversityBraga, Portugalpsa@di.uminho.ptCarlos BaqueroHASLab, INESC TEC & Minho UniversityBraga, Portugalcbm@di.uminho.ptABSTRACTEdge/Fog Computing is an extension to the Cloud Comput-ing model, primarily proposed to pull some of the load oncloud data center towards the edge of the network, i.e., closerto the clients. Despite being a promising model, the founda-tions to adopt and fully exploit the edge model are yet to beclear, and thus new ideas are continuously advocated. In hispaper on “Life beyond Distributed Transactions: an Apos-tate’s Opinion”, Pat Helland proposed his vision to build“al-most infinite” scale future applications, demonstrating whyDistributed Transactions are not very practical under scale.His approach models the applications data state as inde-pendent “entities” with separate serialization scopes, thusallowing efficient local transactions within an entity, but pre-cluding transactions involving different entities. Accessingremote data (which is assumed rare) can be done throughseparate channels in a more message-oriented manner. Inthis paper, we recall Helland’s vision in the aforementionedpaper, explaining how his model fits the Edge ComputingModel either regarding scalability, applications, or assump-tions, and discussing the potential challenges leveraged.KeywordsEdge/Fog Computing, Internet of Things, Scalability1. INTRODUCTIONCloud Computing [3] is an appealing computing modeldue to the pay-per-use business model and the elastic re-source management that is transparent to the user. Thebulk of the storage and computation is mainly done at thecloud data center. The voluminous amounts of data gen-erated by the Internet, especially through the Internet ofThings, data analytics, and social networks leveraged newchallenges to this model as the data center becomes the bot-ACM ISBN 978-1-4503-2138-9.DOI: 10.1145/1235tleneck [9]. The Edge (a.k.a., Fog) Computing [4] model wasproposed to mitigate the load on the cloud center by storingand computing the data at the proximity of the user whenpossible. This brings a lot benefits, among them: (1) Abetter user experience as the response time will be reduced.(2) Balanced load on the cloud data center and peripheralsthrough using the center only when needed. (3) Better se-curity and privacy as the data is stored closer to the clientrather than being exposed in the public cloud. (4) Gooduse of the network as most of the computation can be doneat the edge and often only aggregated data is sent to thecentral cloud [12].On the other hand, the challenges at the edge are vari-ous, and thus no single storage, communication, or computa-tional model is dominating or adopted as a norm. In searchof a data model at the edge, we observed a promising modelthat was introduced, by Pat Helland in his position papertitled “Life Beyond Distributed Transactions: an Apostate’sOpinion” [7], in the area of scalable and distributed appli-cations. In a nutshell, the author presents a model thataims at building “almost infinite” scale applications. Thepaper builds on the argument that distributed transactionson a (shared) data object cannot work in practice at scale.This suggests having separate data abstractions, called “en-tities”, where each entity is a separate scope of serializability(which allows using transactions efficiently). Consequently,this assumes the presence of applications that often accesslocal (to the entity) data, and rarely require remote access(to other entities). In this latter case, communication isdone through separate communication channel abstraction,called “activity”, that assumes at-least-once communicationmedium and implements the desired semantics. This is of-ten implemented by retaining the meta-data that controlsthe inter-entity communication.Being proposed one decade ago, Helland’s vision in hispaper [7] may look an out of the box idea; however, inter-estingly, we believe that this model fits very well the EdgeComputing model. In this paper, we recall the main con-cepts of Pat Helland’s vision, demonstrating how this modelfits the edge. We make more emphasis on the properties andfeatures that are related to the edge application patterns aswell as pointing out the potential challenges and limitations.We plan to perform a comprehensive theoretical and empir-ical study on this subject in the future.In the next section, we summarize the vision of Pat Hel-land in his paper [7] and then discuss the synergizes or issuesthat may exist on the edge; we conclude in Section 3.2. LIFE BEYOND DISTRIBUTED TRANS-ACTIONS: AN EDGE PERSPECTIVEIn this section, we aim at both recalling Helland’s visionin his paper [7], which we refer to as HV to avoid repeti-tion, and demonstrating how it can be applied to the edgecomputing model. To tackle the subject in a comprehensiveway, we organize this section as follow:• We start by showing the motivations and key aspectsthat lead to HV.• We describe the key approach of HV.• And finally, we discuss the assumptions of HV.To keep the subject easy to follow, we adopt this strategy oneach discussed aspect: we summarize the point as proposedin HV, and then we immediately discuss its application andchallenges on the edge. We also try to avoid extensive dis-cussion when we think the addressed point is fairly clear.2.1 The MotivationsHelland’s vision (HV) is driven by two main observations.Limitations of Distributed Transactions.First, the increased business demand for greater scalabil-ity led to realize that distributed transaction are not verypractical at high scale. Some of its symptoms are: bot-tlenecks due to its increased processing overhead, lack ofpartition tolerance, and other disadvantages that makes itfragile and disturb availability. Consequently, almost infinitescalability suggests data designs with separate“fine-grained”items having separate scope of transactions. In this way, atransaction is kept efficient as long as it does not span re-mote items. Dealing with remote data, however, requiresa complementary method through messaging. This view iscommon to edge computing as well. In fact, edge comput-ing is motivated by the fact that many applications mayoften require local data view of an edge region (also calledcloudlet and sometimes simply fog) rather than a full viewof the cloud. The analogy here is that an edge region can beconsidered a separate scope of transaction whereas accessingother edge regions or the cloud will require other messagingchannels. Note that this aspect faces some challenges andlimitations on the edge with highly dynamics nodes and net-work partitions within the same edge region; whereas, it iseasier to deploy on topologies that have fixed membershipand good connectivity.The trend is to scale out.The second observation is that the trend for higher scal-ability can be often described by: “individual things do notget significantly larger, we just get more and more of them”.In fact, this is one of the main reasons towards adoptingthe edge model in which some of the load on the cloud datacenter is pulled to the (plenty of) machines on the edge, es-pecially as the Internet of Things (often composed of maybemillions of“Things”) represents the prime application for theedge. Therefore, in the edge model the dominant pattern isto move from scaling up to scaling out.2.2 The Approach and AbstractionsHV approach divides the data abstractions into indepen-dent items, called “entities”, and messaging controlling statebetween them, called “activities”. The objective is to hope-fully reach linear scalability as items are added.The “Entity” abstraction.In HV, an entity lives on a single machine at a time andthe application can manipulate just one entity at a time.Instead of global transactional serializability, multiple dis-joint scopes of transactional serializability are assumed (dis-cussed later). Only changes in single entities are atomic;atomic transactions cannot span entities, and messaging isused by entities to communicate. In other words, messagesare used to manage the workflow between disjointed trans-actions. From an edge perspective, an entity can live in oneedge region or cloudlet. We generalize this since it is unlikelyto constrain an edge application (e.g., IoT) to span the localdevice only. Note that a region can comprise many enti-ties, provided that they are application-independent. Con-sequently, a transaction can be efficiently applied on an en-tity being executed close to the user with fairly tightly cou-pled machine. The atomicity can however be challenging inloosely coupled edge regions which may require dissemina-tion overlays with stronger guarantees.The “Activity” abstraction.In HV, an activity is an abstraction that controls the in-teraction and communication between entities. Each entitywill have an activity for each partner entity from which itreceives messages. Intuitively, this requires a reliable com-munication medium. HV assumes only at-least-once mes-saging provided that the application tolerates duplicationor messages are idempotent (discussed later). The knowl-edge of the received messages creates some meta-data thatis wrapped up on a per-partner basis. Activities retain thedelivery meta-data to keep track of messages between enti-ties. In the edge model, the activity can be used across enti-ties within a single edge region, since messaging is the normrather than the exception (in order to tolerate the hostilecommunication medium and mobility). Another challengehere is reduce the meta-data stored as some edge devices(e.g., gateways, motes, etc.) have a limited storage capacity.On the other hand, an activity can also be used across edgeregions or the cloud in the applications which occasionallyrequire remote data.2.3 The AssumptionsPat Helland proposed the above approach based on threemain assumptions. We present these assumptions discussinghow they are merely what is assumed in edge applications,and we elaborate on how to guarantee some of them.2.3.1 Multi-layer ArchitectureHV assumes that an application must be divided intoseveral layers, at least two, which differ in their percep-tion of scaling: a lower layer which is scale-aware and anupper scale-agnostic layer. The former directly deals withadding/removing nodes and understands the underlying scaleand guarantees, thus making this scalability transparent tothe upper scale-agnostic layer. This architecture will removethe burden of scalability issues to make the applications easyto program. In edge computing, this assumption also holdsas the design will need to separate the dissemination layerfrom the application layer, otherwise the latter will be verycomplex to develop and maintain, given the hostile commu-nication nature on the edge. We however don’t assume thisis the only paradigm to use. If the application is multi-layered we can build one layer to take care of data that isshared across entities. Although this can be constrained insome cases, different models of the recent synchronization-free computing can be used to maintain high availabilitythrough eventual consistency [11, 2, 5].2.3.2 Scopes of Transactional SerializabilityThe second assumption is to use multiple disjoint scopesof transactional serializability. Each data item resides ina single computer or cluster, and atomic transactions areexecuted within a single scope of transactional serializabil-ity, but not across these disjoint scopes. This can also beassumed for edge applications. When the edge region is al-most centralized, atomicity is easy. Even when the entity isdistributed in a small cluster, serializability can be achievedby using a famous consensus protocols as Paxos [6] as longas the data nodes are close. Therefore, an edge region canbe considered a single scope of serializability, whereas theinteraction across entities is performed through the follow-ing assumption. However, in edge models where hundreds ofnodes are used, it is very hard to achieve atomicity. Alterna-tives can be using recent tree-based gossip protocols that canachieve high performance when possible, and fall back intoa more reliable gossip when the network is unreliable [10].2.3.3 Message Delivery SemanticsHV assumes at-least-once delivery provided that a mes-sage is idempotent. Although most applications may useat-least-once message delivery, which is good with short-lived Unix-style process, sometimes a message may modifysome data durably represented on disk, which can lead tothe situation where the message will be consumed but notacknowledged directly. Even worse, whenever a failure hap-pens, a message may be dropped or duplicated (upon re-transmission). This will require the application to toleratemessage duplication, otherwise exactly-once delivery is re-quired. Although the exactly-once issue is common to anysystem model, it can lead to catastrophic impacts on theedge (i.g., imagine this happening in missile actuators or sen-sors). Classically, exactly-once can be maintained throughkeeping track of delivered messages (e.g., using UDP). This,however, imposes a high storage and time overhead whenlogs get larger. On the edge, this can be worse with theexistence of thousands of devices. On the other hand, re-lying on TCP is not very recommended in hostile environ-ments especially that TCP cannot guarantee exactly-onceunder failure [8]. A possible alternative can be to exper-iment the approach presented in [1] which describes howefficient exactly-once delivery can be maintained even underbad network conditions.3. CONCLUDING REMARKSIn this paper, we recalled Pat Helland’s vision in his paper“Life beyond Distributed Transactions: an Apostate’s Opin-ion” in the area of scalable distributed applications; and weshowed how it fits the edge computing model and where itskews or becomes challenging. We figured out through thisconceptual study that this vision can be very promising as analternative or complementary edge model. Some of the con-cepts in the mentioned paper may not be very practical forthe edge and thus hybrid solutions are advocated (e.g., theuse of Conflict-free Replicated DataTypes to efficiently sharemutable data). Finally, we identified several challenges thatrequire more investigation in the future, among them arethe problems of storage overhead and exactly-once, whichwe are currently working on.4. ACKNOWLEDGMENTSProject “TEC4Growth - Pervasive Intelligence, Enhancersand Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020” is financed by the North Portugal Re-gional Operational Programme (NORTE 2020), under thePORTUGAL 2020 Partnership Agreement, and through theEuropean Regional Development Fund (ERDF).5. REFERENCES[1] Ali Shoker, Paulo Sergio Almeida, and CarlosBaquero. Exactly-Once Quantity Transfer. In In theProceedings of the SRDS Planetary-Scale DistributedSystems Workshop, W-PSDS’15. IEEE-CS, September2015.[2] P. S. Almeida, A. Shoker, and C. Baquero. Delta statereplicated data types. CoRR, abs/1603.01529, 2016.[3] M. Armbrust, I. Stoica, M. Zaharia, A. Fox,R. Griffith, A. D. Joseph, R. Katz, A. Konwinski,G. Lee, D. Patterson, and A. Rabkin. A view of cloudcomputing. Communications of the ACM, 53(4):50,apr 2010.[4] F. Bonomi, R. Milito, J. Zhu, and S. Addepalli. FogComputing and Its Role in the Internet of Things.Proceedings of the first edition of the MCC workshopon Mobile cloud computing, pages 13–16, 2012.[5] P. S. A. Carlos Baquero and A. Shoker. Makingoperation-based crdts operation-based. In DistributedApplications and Interoperable Systems - 14th IFIPWG 6.1 International Conference, DAIS’14Proceedings, Berlin, Germany, pages 126–140, 2014.[6] T. D. Chandra, R. Griesemer, and J. Redstone. Paxosmade live: an engineering perspective. In Proceedingsof the twenty-sixth annual ACM symposium onPrinciples of distributed computing, pages 398–407.ACM, 2007.[7] P. Helland. Life beyond distributed transactions: anapostate’s opinion. In CIDR, pages 132–141, 2007.[8] P. Helland. Idempotence is not a medical condition.Communications of the ACM, 55(5):56–65, 2012.[9] IBM. Bringing big data to the enterprise, 2016.[10] J. Leita˜o, J. Pereira, and L. Rodrigues. Epidemicbroadcast trees. pages 301–310, Oct. 2007.[11] M. Shapiro, N. Preguic¸a, C. Baquero, andM. Zawirski. Conflict-free replicated data types.Technical Report RR-7687, INRIA, July 2011.[12] Steve Wexler. Dell Does IoT. . . Totally!http://it-tna.com/2015/06/01/dell-does-iot-totally/,2015.

Life beyond distributed transactions on the edge

Ali Shoker

Ziad Kassam

Paulo Sérgio Almeida

Carlos Baquero

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Life Beyond Distributed Transactions on the Edge

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Life beyond distributed transactions on the edge

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