Scheduling highly available applications on cloud environments

Cloud computing is becoming a popular solution for storing data and executing applications due to its on-demand pay-per-use policy that allows access to virtually unlimited resources. In this frame applications such as those oriented towards Web 2.0 begin to be migrated on cloud systems. Web 2.0 applications are usually composed of several components that run indefinitely and need to be available to end users throughout their execution life cycle. Their availability strongly depends on the number of resource failures and on the variation in user hit rate. These problems are usually solved through scaling. A scaled application can span its components on several nodes. Hence if one or more nodes fail it could become unavailable. Therefore we require a method of ensuring the application’s functionality despite the number of node failures. In this paper we propose to build highly available applications, i.e., systems with low downtimes, by taking advantage of the component based architecture and of the application scaling property. We present a solution to finding the optimal number of component types needed on nodes so that every type is present on every allocated node. Furthermore nodes cannot exceed a maximum threshold and the total running cost of the applications needs to be minimized. A sub-optimal solution is also given. Both solutions rely on genetic algorithms to achieve their goals. The efficiency of the sub-optimal algorithm is studied with respect to its success rate, i.e., probability of the schedule to provide highly available applications in case all but one node fail. Tests performed on the sub-optimal algorithm in terms of node load, closeness to the optimal solution and success rate prove the algorithm’s efficiency

A Self-adaptive Agent-based System for Cloud Platforms

Cloud computing is a model for enabling on-demand network access to a shared pool of computing resources, that can be dynamically allocated and released with minimal effort. However, this task can be complex in highly dynamic environments with various resources to allocate for an increasing number of different users requirements. In this work, we propose a Cloud architecture based on a multi-agent system exhibiting a self-adaptive behavior to address the dynamic resource allocation. This self-adaptive system follows a MAPE-K approach to reason and act, according to QoS, Cloud service information, and propagated run-time information, to detect QoS degradation and make better resource allocation decisions. We validate our proposed Cloud architecture by simulation. Results show that it can properly allocate resources to reduce energy consumption, while satisfying the users demanded QoS

Towards delay-aware container-based Service Function Chaining in Fog Computing

Recently, the fifth-generation mobile network (5G) is getting significant attention. Empowered by Network Function Virtualization (NFV), 5G networks aim to support diverse services coming from different business verticals (e.g. Smart Cities, Automotive, etc). To fully leverage on NFV, services must be connected in a specific order forming a Service Function Chain (SFC). SFCs allow mobile operators to benefit from the high flexibility and low operational costs introduced by network softwarization. Additionally, Cloud computing is evolving towards a distributed paradigm called Fog Computing, which aims to provide a distributed cloud infrastructure by placing computational resources close to end-users. However, most SFC research only focuses on Multi-access Edge Computing (MEC) use cases where mobile operators aim to deploy services close to end-users. Bi-directional communication between Edges and Cloud are not considered in MEC, which in contrast is highly important in a Fog environment as in distributed anomaly detection services. Therefore, in this paper, we propose an SFC controller to optimize the placement of service chains in Fog environments, specifically tailored for Smart City use cases. Our approach has been validated on the Kubernetes platform, an open-source orchestrator for the automatic deployment of micro-services. Our SFC controller has been implemented as an extension to the scheduling features available in Kubernetes, enabling the efficient provisioning of container-based SFCs while optimizing resource allocation and reducing the end-to-end (E2E) latency. Results show that the proposed approach can lower the network latency up to 18% for the studied use case while conserving bandwidth when compared to the default scheduling mechanism