Edge computing promises to bring computation close to the end-users to support emergent applications such as virtual reality. However, the computational capacity at the edge of the network is currently limited. To become a pervasive paradigm, edge computing needs highly dispersed decentralized deployments, that, contrary to cloud, cannot benefit from economies of scale. In this situation, crowdsourcing appears attractive - there are plenty of computing devices at the disposal of the general public, and these devices are located exactly where computing power is needed the most - at the edge of the network. Crowdsourcing has been a success maker for scientific computing projects, e.g., SETI@home, or distributed ledger systems empowering decentralized finance. However, as of now, there is no crowdsourced system that addresses the needs of edge computing. In this position paper, we aim to identify the causes of this shortcoming, analyze the potential ways to overcome it, and outline future directions.@en

AWS offers discounted transient virtual instances as a way to sell unused resources in their data-centers, and users can enjoy up to 90% discount as compared to the regular on-demand pricing. Despite the economic incentives to purchase these transient instances, they do not come with regular availability SLAs, meaning that they can be evicted at any moment. Hence, the user is responsible for managing the instance availability to meet the application requirements. In this paper, we present Bricklayer, a software tool that assists users to better use transient resources in the cloud, reducing costs for the same amount of resources, and increasing the overall instance availability. Bricklayer searches for possible combinations of smaller and cheaper instances to compose the requested amount of resources while deploying them into different spot markets to reduce the risk of eviction. We implemented and evaluated Bricklayer using 3 months of historical data from AWS and found out that it can reduce up 54% of the regular spot price and up to 95% compared to the standard on-demand pricing.

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The sharing economy has made great inroads with services like Uber or Airbnb enabling people to share their unused resources with those needing them. The computing world, however, despite its abundance of excess computational resources has remained largely unaffected by this trend, save for few examples like SETI@home. We present DeCloud, a decentralized market framework bringing the sharing economy to on-demand computing where the offering of pay-as-you-go services will not be limited to large companies, but ad hoc clouds can be spontaneously formed on the edge of the network. We design incentive compatible double auction mechanism targeted specifically for distributed ledger trust model instead of relying on third-party auctioneer. DeCloud incorporates innovative matching heuristic capable of coping with the level of heterogeneity inherent for large-scale open systems. Evaluating DeCloud on Google cluster-usage data, we demonstrate that the system has a near-optimal performance from an economic point of view, additionally enhanced by the flexibility of matching.

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Edge computing provides an attractive platform for bringing data and processing closer to users in networked environments. Several edge proposals aim to place the edge servers at a couple hop distance from the client to ensure lowest possible compute and network delay. An attractive edge server placement is to co-locate it with existing (cellular) base stations to avoid additional infrastructure establishment costs. However, determining the exact locations for edge servers is an important question that must be resolved for optimal placement. In this paper, we present Anveshak1, a framework that solves the problem of placing edge servers in a geographical topology and provides the optimal solution for edge providers. Our proposed solution considers both end-user application requirements as well as deployment and operating costs incurred by edge platform providers. The placement optimization metric of Anveshak considers the request pattern of users and existing user-established edge servers. In our evaluation based on real datasets, we show that Anveshak achieves 67% increase in user satisfaction while maintaining high server utilization.@en

Edge clouds handle data and computations closer to its source and users. Applications like industrial automation, bring new challenges and require solutions tailored for computation-centric edge cloud networks. In this paper we build on existing edge and fog computing models and develop a solution to predict and store data in edge resource caches for upcoming computations. Our solution is based on grouping caches according to the workloads they serve. We further develop methods for populating the caches and ensuring the coherence of the cached data. We evaluate the performance of our grouping mechanisms and show that they bring significant performance gains, both in terms of network traffic and access latency.@en