Edge computing aims to enable applications with stringent latency requirements, e.g., augmented reality, and tame the overwhelming data streams generated by IoT devices. A core principle of this paradigm is to bring the computation from a distant cloud closer to service consumers and data producers. Consequentially, the issue of edge computing facilities' placement arises. We present a comprehensive analysis suggesting where to place general-purpose edge computing resources on an Internet-wide scale. We base our conclusions on extensive real-world network measurements. We perform extensive traceroute measurements from RIPE Atlas to datacenters in the US, resulting in a graph of 11K routers. We identify the affiliations of the routers to determine the network providers that can act as edge providers. We devise several edge placement strategies and show that they can improve cloud access latency by up to 30%.

Multipath TCP (MPTCP) extends traditional TCP to enable simultaneous use of multiple connection endpoints at the source and destination. MPTCP has been under active development since its standardization in 2013, and more recently in February 2020, MPTCP was upstreamed to the Linux kernel. In this paper, we provide the first broad analysis of MPTCPv0 in the Internet. We probe the entire IPv4 address space and an IPv6 hitlist to detect MPTCP-enabled systems operational on port 80 and 443. Our scans reveal a steady increase in MPTCP-capable IPs, reaching 9k+ on IPv4 and a few dozen on IPv6. We also discover a significant share of seemingly MPTCP-capable hosts, an artifact of middleboxes mirroring TCP options. We conduct targeted HTTP(S) measurements towards select hosts and find that middleboxes can aggressively impact the perceived quality of applications utilizing MPTCP. Finally, we analyze two complementary traffic traces from CAIDA and MAWI to shed light on the real-world usage of MPTCP. We find that while MPTCP usage has increased by a factor of 20 over the past few years, its traffic share is still quite low.

Amazon Web Services (AWS) offers transient virtual servers at a discounted price as a way to sell unused spare capacity in its data centers. Although transient servers are very appealing as some instances have up to 90% discount, they are not bound to regular availability guarantees as they are opportunistic resources sold on the spot market. In this paper, we present SPA, a framework that remarkably increases the spot instance reliability over time due to insights gained from the analysis of historical data, such as cross-region price variability and intervals between evictions. We implemented the SPA reliability strategy, evaluated them using over one year of historical pricing data from AWS, and found out that we can increase the transient instance lifetime by adding a pricing overhead of 3.5% in the spot price in the best scenario.

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.

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.