This paper proposes a combinatorial auction-based marketplace mechanism for cloud computing services, which allows users to reserve arbitrary combination of services at requested timeslots, prices and quality of service. The proposed mechanism helps enterprise users build workflow applications in a cloud computing environment, specifically on the platform-as-a-service, where the users need to compose multiple types of services at different timeslots. The proposed marketplace mechanism consists of a forward market for an advance reservation and a spot market for immediate allocation of services. Each market employs mixed integer programming to enforce a Pareto optimum allocation with maximized social economic welfare, as well as double-sided auction design to encourage both users and providers to compete for buying and selling the services. The evaluation results show that (1) the proposed forward/combinatorial mechanism outperforms other non-combinatorial and/or non-reservation (spot) mechanisms in both user-centric rationality and global efficiency, and (2) running both a forward market and a spot market improves utilization without disturbing advance reservations depending on the provider's policy.

Linux container technology and clusters of the containers are expected to make web services consisting of multiple web servers and a load balancer portable, and thus realize easy migration of web services across the different cloud providers and on-premise datacenters. This prevents service to be locked-in a single cloud provider or a single location and enables users to meet their business needs, e.g., preparing for a natural disaster. However existing container management systems lack the generic implementation to route the traffic from the internet into the web service consisting of container clusters. For example, Kubernetes, which is one of the most popular container management systems, is heavily dependent on cloud load balancers. If users use unsupported cloud providers or on-premise datacenters, it is up to users to route the traffic into their cluster while keeping the redundancy and scalability. This means that users could easily be locked-in the major cloud providers including GCP, AWS, and Azure. In this paper, we propose an architecture for a group of containerized load balancers with ECMP redundancy. We containerize Linux ipvs and exabgp, and then implement an experimental system using standard Linux boxes and open source software. We also reveal that our proposed system properly route the traffics with redundancy. Our proposed load balancers are usable even if the infrastructure does not have supported load balancers by Kubernetes and thus free users from lock-ins.

This paper describes an architectural design and related services of a new Japanese academic backbone network, called SINET5, which will be launched in April 2016. The network will cover all 47 prefectures with 100-Gigabit Ethernet technology and connect each pair of prefectures with a minimized latency. This will enable users to leverage evolving cloud-computing powers as well as draw on a high-performance platform for data-intensive applications. The transmission layer will form a fully meshed, SDN-friendly, and reliable network. The services will evolve to be more dynamic and cloud-oriented in response to user demands. Cyber-security measures for the backbone network and tools for performance acceleration and visualization are also discussed.

Cloud computing is a widely used computing platform in business and academic communities. Performance is an important issue when a user runs an application in the cloud. The user may want to estimate the application-execution time beforehand to guarantee the application performance or to choose the most suitable cloud. Moreover, the cloud system architect and the designer need to understand the application performance characteristics, such as the scalability or the utilization of cloud platforms, to improve performance. However, because the application performance in clouds sometime fluctuates, estimation of the application performance is difficult. In this paper, we discuss the performance fluctuation of Hadoop jobs in both a public cloud and a community cloud for one to three months. The experimental results indicate phenomena that we cannot see without long-term experiments and phenomena inherent in Hadoop. The results suggest better ways to estimate Hadoop application performances in clouds. For example, we should be aware of application characteristics (CPU intensive or communication intensive), datacenter characteristics (busy or not), and time frame (time of day and day of the week) to estimate the performance fluctuation due to workload congestion in cloud platforms. Furthermore, we should be aware of performance degradation due to task re-execution in Hadoop applications.